Search Results (2,098)

This study aims at developing an integrated system comprising TDR technologies for continuous and 3D monitoring of the vadose zone with special focus on the aerial distribution of water during an artificial sprinkling experiment. The system was tested during field artificial infiltration experiments. The objective of this study is to evaluate a flexible long TDR sensor in the field during a sprinkling and infiltration experiment that mimics rainfall and irrigation events through zonal wetting, monitor the resulting water flows and compare the findings with those from custom rigid spatial TDR sensors. This study exclusively used the TDR technique to measure soil moisture changes during the infiltration experiment, utilizing both custom rigid spatial sensors and a flexible sensor. The results indicate that the flexible sensor, which can be installed in the soil in arrays that rigid sensors cannot, achieved logical and coherent soil moisture estimations, proving that it could also be used as a standalone sensor for soil volumetric water content measurements. The use of long flexible sensors, along with long rigid sensors, facilitates continuous, precise, and 3D monitoring of moisture changes across larger soil volumes, transcending traditional point measurements and 1D soil moisture profiles typically associated with the TDR technique. Full article

Rain-on-snow events in cold regions frequently trigger slope failures. This study elucidates the instability mechanism of low-permeability silty clay slopes under combined rainfall and snowmelt conditions. A refined numerical model was established based on the sequential coupling of SEEP/W and SLOPE/W, utilizing the Morgenstern-Price method for stability analysis. A rigorous mesh sensitivity analysis confirmed that a locally refined mesh of 0.2 m with exponential time-stepping is essential to eliminate numerical dispersion at the wetting front. Simulation results indicate a significant time-lag effect in stability response; the critical failure time lags behind rainfall cessation (e.g., ~8 h for moderate rain) due to gravity-driven moisture redistribution. Spatially, the slope toe reaches saturation first, generating excess pore-water pressure and suggesting a tendency toward retrogressive instability. Furthermore, snowmelt superposition functions as a continuous hydraulic load, creating a base flow effect that advances the acceleration phase of failure by 1–2 h and further reduces the minimum safety factor. These findings highlight the critical role of the slope toe saturation and the necessity of considering snowmelt intensity in landslide early warning systems for cold regions. Full article

Soil moisture and saturation are crucial hydrological variables for understanding the soil’s condition and modeling improvement. The National Water Model (NWM), a large-scale model, simulates the hydrologic cycle across the Contiguous United States, Hawaii, and Puerto Rico. The study’s objective was to evaluate the NWM’s performance in estimating and forecasting soil moisture in Puerto Rico from the year 2021 to 2023. The datasets used included in situ stations, model outputs, and remotely sensed data from the Soil Moisture Active Passive (SMAP) mission. Then, we used Volumetric bias (Vbias), Mean Absolute Error (MAE), and Kling–Gupta Efficiency (KGE) to measure performance. The analysis assimilation results showed that three stations in each dataset had an inversely predominant error equal to 25% or less. This low error was reflected in the obtained Vbias and MAE results. Meanwhile, the KGE analysis indicated that the NWM achieves low to moderate soil moisture performance, with better agreement against SMAP than in situ observations. However, the forecasted datasets did not produce satisfactory results. Short-range forecasts exhibited negative KGE values, highlighting the importance of data assimilation, the persistent influence of bias, and scale mismatch. Although the NWM’s primary focus is streamflow forecast, these findings highlight the potential application of the model beyond its primary focus. Full article

Portable near-infrared (NIR) spectroscopy devices offer the advantages of rapid, non-destructive, and versatile coal quality analysis. However, in complex mining environments, variations in the probe–sample distance can cause significant spectral distortions, resulting in severe distribution shifts between the source and target domains and thus limiting model generalization. In practical industrial scenarios, target-domain data are often unavailable, making conventional domain adaptation methods that rely on target samples difficult to apply. To address this challenge, this paper proposes a target-free multi-source domain adaptation framework tailored for portable device distance-shift scenarios to achieve robust prediction of coal air-dried moisture (Mad). Under a multi-source joint learning strategy, the framework aligns cross-domain features through adversarial training and distribution matching, while a spectroscopy-specific data augmentation strategy is designed to simulate realistic measurement disturbances such as noise perturbation, baseline drift, and wavelength shift, thereby enhancing the model’s robustness from the source side. In addition, a Mad-aware triplet loss function is introduced to establish a balanced constraint between task consistency and domain invariance, effectively improving cross-domain generalization capability. Experimental results on multi-distance NIR datasets show that the proposed method significantly outperforms representative comparison algorithms in terms of $R^2$, RMSE, and MAE, verifying that the framework effectively mitigates the effects of probe–sample distance shifts under target-free conditions and achieves high-precision coal moisture prediction. Full article

In order to improve the control accuracy and adaptability of drying control systems in complex paddy rice drying processes, the Gaussian process regression model predictive (GPR-MPC) drying process control strategy is designed. The strategy integrates the advantages of drying mathematical models and artificial intelligence algorithms. Firstly, based on the predicted moisture content of the drying mathematical model and the moisture content detection value, the Gaussian process regression is used to establish the model of moisture content prediction error. Secondly, the GPR-MPC control system is designed and simulation experiments are conducted to verify its effectiveness. Finally, the GPR-MPC intelligent control system of a grain dryer is designed and drying experiments are conducted with the grain dryer. The GPR-MPC intelligent control system testing experiment is conducted using a 15-ton cross-flow batch type recirculating grain dryer. The experimental result shows that the maximum, average, and variance of the grain moisture content control error are 0.4%, 0.165% and 0.114%, respectively. Compared to the MPC control system, the designed GPR-MPC intelligent control system has high prediction accuracy, small moisture content control error, and stable control system operation. The integration of drying mathematical models and artificial intelligence algorithms can effectively improve the drying effect and reduce dependence on data volume. This research is of great significance for promoting the development of intelligent drying technology. Full article

This study calibrated discrete element parameters for organic fertilizer (OF) and compost fertilizer (CF) to support spreading equipment design. Using the Hertz–Mindlin with JKR model, DEM simulations were integrated with physical angle of repose measurements. Parameters were systematically optimized via Plackett–Burman screening, steepest ascent, and Box–Behnken response surface methodology. Results indicated distinct moisture-sensitive behaviors: OF exhibited monotonic increases in dynamic friction coefficient (0.223–0.362) and JKR surface energy (0.064–0.166 J/m²), whereas CF showed nonlinear friction trends with surface energy rising from 0.209 to 0.326 J/m². A predictive model directly linking moisture content to DEM parameters was established using the cylinder-lifting method. Validation confirmed model accuracy, with angle of repose errors of 2.57% (OF) and 4.05% (CF). Simulated spreading widths closely matched field data, showing relative errors below 8%. The calibrated DEM framework provides a reliable basis for optimizing organic manure spreader performance. Full article

Non-point source pollution has become one of the most widespread environmental degradation problems in recent years. This study aimed to investigate how hydrological processes regulate nitrogen and phosphorus losses under simulated rainfall conditions through in situ rainfall experiments in karst farmland. We conducted a field-scale plot experiment, recorded rainfall and runoff, and measured the nutrient concentration in the runoff of nine experimental plots on the slope toe, middle slope and upper slope. Simulated rainfall intensity was 90 mm/h for 60 min. The results showed nitrogen losses were dominated by subsurface flow in small-scale studies, which accounted for 55.19% (2.50 kg/ha), 71.35% (3.88 kg/ha), and 93.85% (1.39 kg/ha) of TN losses at the toe, middle, and upper slope positions, respectively. The middle slope exhibited the highest losses of N mainly due to its larger subsurface runoff volume. NH⁴⁺ dominated TN in surface flow, contributing up to 97.5% (0.0092 kg/ha) at the slope toe, whereas NO³⁻ was the dominant N form in subsurface flow, with little variation across the three slope positions, averaging 0.062 kg/ha. In contrast, phosphorus losses are primarily associated with surface flow, with TP concentrations in surface flow being 5–60 times higher than those in subsurface flow, with average surface TP losses of approximately 0.04 kg/ha. These results imply that nutrient management in karst farmland should adopt differentiated control strategies, with greater emphasis on reducing subsurface nitrogen leaching while limiting surface runoff and erosion to mitigate phosphorus losses. However, the conclusions are based solely on small-scale rainfall simulation experiments, and nutrient loss may also be influenced by factors such as karst terrain heterogeneity, prior soil moisture content, soil properties, and rainfall characteristics. Full article

This study investigated an intense and unusual summer transboundary dust storm event that occurred between 21 and 23 June 2024. By integrating remote sensing observations, reanalysis data, WRF-Chem simulations, and LAGRANTO trajectory tracking, we systematically revealed the dust emission, transport, deposition, and formation mechanisms of this event. The dust primarily originated from the Gobi region of southern Mongolia, where concentrations exceeded 10,000 µg m⁻³, and decayed exponentially as the Mongolian cyclone moved southeastward. Post border-crossing into China, the event transitioned to blowing and floating dust, with concentrations decreasing significantly. During transport, dry deposition dominated the source area and the frontal part of the transport path in the early stages, while wet deposition was associated with the precipitation system of the Mongolian cyclone and concentrated north and east of the cyclone’s track. On 21 June 2024, the average wind speed in the source region reached 11.35 ms⁻¹, the highest recorded in the past 45 years. This was attributed to surface anomalies, including reduced soil moisture, poor vegetation cover, higher temperatures, and decreased precipitation relative to the multi-year average. The comprehensive application of multi-source data and models in this work elucidates the full lifecycle of this rare summer dust event, providing scientific insights into the atmospheric processes governing extreme dust events and their transboundary impacts. Full article

Thermal stress in hot–humid urban environments constitutes a persistent sustainability challenge, driven by the interaction of extreme temperatures, high atmospheric moisture, and heat-retaining urban surfaces, which collectively intensify outdoor discomfort and increase cooling-energy demand. Within this context, soft landscape systems have gained recognition as nature-based solutions capable of moderating microclimates and enhancing residential livability; however, their systematic prioritization based on integrated sustainability performance remains insufficiently addressed, particularly in Gulf-region residential developments. This study proposes a sustainability-oriented decision-support framework that evaluates and prioritizes soft landscape strategies for thermal comfort enhancement using the Analytic Hierarchy Process (AHP) as the core analytical method. Expert judgments were elicited and structured across five sustainability-driven criteria—shading effectiveness, evapotranspiration potential, airflow facilitation, aesthetic–psychological comfort, and implementation and maintenance cost—and applied to five soft landscape alternatives. To verify the physical plausibility of the expert-derived prioritization, microclimate simulations were conducted using ENVI-met under extreme summer conditions, representing the hottest day of the year, at key diurnal intervals. The results reveal a clear dominance of shading-based mechanisms, with tree canopy systems emerging as the most effective and sustainable intervention due to their superior radiative control, ecological cooling capacity, and perceptual benefits. Simulation outputs confirm that canopy-driven strategies achieve the most substantial reductions in mean radiant temperature during peak thermal stress, while surface-based interventions provide secondary benefits primarily related to diurnal heat dissipation. At peak thermal stress (14:00), Scenario 2 reduced mean radiant temperature (MRT) from 71.69 °C to 54.23 °C (≈24% reduction) and PMV from 7.33 to 5.70 (≈22% reduction) relative to existing conditions. By integrating expert-based multi-criteria evaluation with simulation-based thermal verification, the study advances a robust and transferable framework for climate-responsive residential landscape planning. The findings reposition soft landscape systems as essential climatic infrastructure, offering actionable guidance for enhancing thermal resilience, reducing cooling-energy dependence, and supporting sustainable residential development in hot–humid regions. Full article

This study presents the Moisture Dynamic Model (MDM), a new semi-physical formulation designed to estimate Fuel Moisture Content (FMC) using only air temperature and relative humidity. The core innovation of this work lies in the introduction of an Arrhenius-type kinetic term into a fuel moisture prediction framework, allowing temperature-driven desorption processes to be explicitly represented within a lightweight operational model. Its predictive capability was assessed through experimental campaigns on Cistus monspeliensis shrublands in Corsica and validated using FireStar3D simulations. A second major contribution is the coupling of the MDM with the physical wildfire simulator FireStar3D to quantify how FMC prediction errors propagate into fire spread predictions. The MDM accurately reproduced the seasonal variability of FMC, achieving strong correlation with experimental data during dry summer periods. When coupled with FireStar3D, discrepancies in the predicted rate of spread remained below 4% under high-risk meteorological conditions. While the model performed robustly during summer, its accuracy decreased during spring, when rainfall events and microclimatic variability introduced greater uncertainty. This work represents a proof of concept demonstrating the potential of a simple physically interpretable FMC model for operational fire behaviour prediction. Full article

Precision irrigation is crucial for sustainable agriculture, yet conventional single-objective optimization methods struggle to balance conflicting demands such as crop yield, operational cost, and environmental sustainability. This study introduces an Adaptive Multi-Strategy Grey Wolf Optimizer with Reinforcement Learning for Multi-Objective Optimization (AMSGWO-RL-MO) to enhance precision irrigation decision-making. AMSGWO-RL-MO integrates four strategies: standard GWO exploitation, Lévy flight exploration, differential evolution-based diversity enhancement, and Stochastic Elite Opposition-Based Learning. A Q-learning mechanism dynamically adjusts these strategies, adapting to real-time search conditions to select the optimal approach. We constructed a comprehensive three-objective framework incorporating soil moisture dynamics, crop growth models, and environmental impact assessments. Experimental simulations over a 40-day growth cycle demonstrate AMSGWO-RL-MO’s rapid convergence by the sixth generation, consistently achieving a high-quality Pareto front across 30 independent runs. The knee-point solution yielded a mean crop yield of 96.96%, outperforming standard GWO and multi-strategy variants by approximately 3.8%. Statistical analysis confirms its superior robustness and well-distributed solutions along the Pareto front. These results indicate that the RL-driven adaptive mechanism effectively balances exploration and exploitation. The proposed method offers a more diverse array of Pareto-optimal solutions, presenting a broader trade-off space for balancing crop yield and environmental sustainability compared to traditional weighted-sum approaches. This enhancement facilitates scientific agricultural decision-making under various operational constraints. Full article

The Water Cloud Model (WCM) provides a simple and effective framework for quantifying vegetation canopy absorption and scattering components and has been widely applied to active microwave-based soil moisture retrieval on vegetated areas. However, under conditions of dense vegetation, the WCM neglects soil–vegetation interaction scattering, which limits its retrieval accuracy. To mitigate this limitation, this study analyzes the active microwave radiative transfer process under vegetated conditions and proposes an approach to explicitly quantify soil–vegetation interaction scattering by incorporating the first-order soil–vegetation-scattering component into the WCM, thereby enhancing the performance of the WCM at high vegetation coverage. The effectiveness of the proposed model is validated using in situ observations from three study areas with different vegetation characteristics: (a) a pure farmland area, (b) a mixed landscape with small forest and shrubland patches and large cropland areas, and (c) a mixed landscape with large forest and shrubland patches and small cropland areas. Data from 2020–2022 were used for model training and parameter calibration, while independent datasets from 2023 and 2024 were employed to validate the model performance. In both the model training and validation phases, the proposed model improved the soil moisture retrieval accuracy across all study areas while exhibiting slight differences in the backscatter simulation performance. During the model training period, the root-mean-square error (RMSE) between simulated and measured backscatter in study area (a) increased slightly by 1.9%, whereas it decreased by 2.79% and 2.0% in study areas (b) and (c), respectively. In terms of soil moisture retrieval, the RMSEs in study areas (a), (b), and (c) decreased by 6.66%, 1.18%, and 6.03%, respectively. In the validation experiments, for the year 2023, the RMSEs of simulated versus observed backscatter in study areas (a), (b), and (c) were reduced by 9.6%, 1.51%, and 4.35%, respectively, while the corresponding soil moisture retrieval RMSEs decreased by 12.6%, 4.53%, and 7.24%. For the year 2024, the backscatter RMSE in study area (a) increased by 6.07%, whereas it decreased by 2.17% and 6.47% in study areas (b) and (c), respectively; meanwhile, the soil moisture retrieval RMSEs were reduced by 2.81%, 3.69%, and 9.45%, respectively. In summary, this study improves the accuracy of active microwave remote sensing-based soil moisture retrieval in areas with different vegetation cover by explicitly quantifying soil–vegetation interaction scattering. Full article

Nutrients recovered from municipal and dairy wastewaters in a bioelectrochemical system constructed with terracotta and biochar were used in different soil amendments. These amendments included addition of terracotta (TS), biochar (BS), terracotta and biochar nutrient-rich mixtures from bioelectrochemical systems, DWW (dairy wastewater), and SWW (synthetic wastewater), respectively. Corn growth affected by these amendments was compared with control, termed straight soil (SS). The first experimental setup consisted of 60 plants, four replications per group, and nutrient loadings (0%, 50%, and 100% Hoagland Nutrient Solution, HNS) in the fall season. After harvesting, the plants and soil were analyzed for agro-physical characteristics by various methods. At the 100% nutrient treatment, the TS soil had the best yielding plants. Overall, plants grown in DWW and SWW soil amendments with 0% and 50% nutrient treatments had the best results in plant height, total plant dry weight, the average number of leaves per plant, leaf surface area, shoot dry weight, root/shoot ratio, root surface area, and NBI when compared to the control group. Another test was carried out with 80 corn plants grown using five different soil mediums and using four different nutrient treatments in the spring season. Twenty of the plants were put through a simulated drought to evaluate drought resistance (as measured by plant growth) in different soil amendments. In this test, the SWW soil amendment had a negative effect at 0% HNS and in warm weather. The SWW soil medium had large retention in soil moisture, which had a negative growth effect. It is recommended that the irrigation be monitored closely when applying the SWW soil amendment to avoid overwatering. This research provides critical insights into nutrient reuse in crop production. Full article

To address the lack of contact and breakage parameters in the discrete element method (DEM) simulation of potato seed cutting and sorting processes, this study took the ‘Atlantic’ potato seed as the research object and constructed a crushable potato model using EDEM. Through physical experiments, the mean average diameter, moisture content, density, Poisson’s ratio, and elastic modulus were measured. The coefficients of collision restitution, static friction, and rolling friction between the potato seed and the Q235 steel plate were determined as 0.576, 0.559, and 0.073, respectively. Using the actual repose angle of 22.89° as the response target, and combining the steepest ascent test with the Box–Behnken design, the non-cohesive contact parameters between potato seed particles were calibrated. The resulting coefficients of collision restitution, static friction, and rolling friction between particles were determined as 0.404, 0.412, and 0.0156, respectively. Finally, based on physical shear tests (maximum shear force 23.56 N), significant influencing factors were identified through Plackett–Burman screening as the bonding radius ratio r and the normal stiffness per unit area Kn. Using the steepest ascent test and the Box–Behnken response surface method, the key bonding parameters of the Bonding V2 model were calibrated as follows: r = 1.098, K_n = 8.597 × 10⁷ N·mm⁻³, tangential stiffness per unit area K_t = 3.250 × 10⁶ N·mm⁻³, critical compressive stress σ_n = 5.500 × 10⁵ Pa, and shear strength τ_t = 3.000 × 10⁴ Pa. The relative error between the simulated and actual maximum shear forces was 0.89%, which is small. The calibrated flexible model accurately represents the physical characteristics of potato seeds and provides a reliable reference for the design of mechanized potato seed cutting and sorting equipment. Full article

Low-level jets (LLJs) are important mesoscale features in the Arctic and are highly relevant for the atmospheric transport of heat, moisture, and air pollutants, as well as for wind energy and aircraft operations. In this paper, LLJs at the Tiksi observatory in the Laptev Sea region are investigated during the period 2014–2020 using simulations performed with the regional climate model CCLM with a 5 km resolution. The main synoptic weather patterns for LLJs at Tiksi were identified using a self-organizing map (SOM) analysis. LLJs occurred in about 55% of all profiles with an average height of about 400 m and an average speed of about 13 m/s. About 60% of the LLJs had core speeds larger than 10 m/s (strong jets). The occurrence frequency for all jets showed a pronounced seasonal cycle with more and stronger LLJs during winter. The turbulent kinetic energy in the lower ABL was four times as large for LLJs than for situations without LLJs, which underlines the impact of LLJs on turbulent processes in the ABL. The mean duration of LLJ events (duration of at least 6 h) was almost 24 h and the 90th percentile was about two days. About 70% of the LLJ events were associated with downslope winds of the local mountain ridge and had a longer duration of about three days for the 90th percentile. Full article